{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acd3c386",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7cc517d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import mplfinance as mpf\n",
    "import talib as ta\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cc32d1e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def detect_macd_signals(data):\n",
    "    \"\"\"Use MACD cross-over to decide buy/sell\n",
    "\n",
    "    Args:\n",
    "      data: panda DataFrame with OHLC with MACD data\n",
    "    \n",
    "    Return:\n",
    "      buy_signals, sell_signals: for chart plot\n",
    "      signals: buy/sell transaction for summary printing\n",
    "    \"\"\"\n",
    "\n",
    "    buy_signals = [np.nan]\n",
    "    sell_signals = [np.nan]\n",
    "    signals = []\n",
    "    last_signal = None\n",
    "\n",
    "    for i in range(1, len(data)):\n",
    "        if data['macd_hist'][i-1] < 0 and data['macd_hist'][i] > 0:\n",
    "            price = (data['Open'][i] + data['Close'][i]) / 2\n",
    "            buy_signals.append(price)\n",
    "            last_signal = 'buy'\n",
    "            signals.append({\n",
    "                'date': data.index[i],\n",
    "                'action': 'buy',\n",
    "                'price': price\n",
    "                })            \n",
    "            sell_signals.append(np.nan)\n",
    "        elif data['macd_hist'][i-1] > 0 and data['macd_hist'][i] < 0 and last_signal == 'buy':\n",
    "            price = (data['Open'][i] + data['Close'][i]) / 2\n",
    "            sell_signals.append(price)\n",
    "            last_signal = 'sell'\n",
    "            signals.append({\n",
    "                'date': data.index[i],\n",
    "                'action': 'sell',\n",
    "                'price': price\n",
    "                })            \n",
    "            buy_signals.append(np.nan)\n",
    "        else:\n",
    "            buy_signals.append(np.nan)\n",
    "            sell_signals.append(np.nan)\n",
    "\n",
    "    return buy_signals, sell_signals, signals\n",
    "def print_performance_summary(signals):\n",
    "    \"\"\"Print buy/sell transactions and statistics\n",
    "    \n",
    "    Args:\n",
    "      signals: recorded buy/sell transactions\n",
    "    \"\"\"\n",
    "    pairs = zip(*[iter(signals)]*2)\n",
    "    rows = []\n",
    "\n",
    "    profit_count = 0\n",
    "    profit_pct_avg = 0\n",
    "\n",
    "    for (buy, sell) in pairs:\n",
    "        profit = sell['price'] - buy['price']\n",
    "        profit_pct = profit / buy['price']\n",
    "\n",
    "        if profit > 0:\n",
    "            profit_count += 1\n",
    "        profit_pct_avg += profit_pct\n",
    "\n",
    "        row = {\n",
    "            'buy_date': buy['date'],\n",
    "            'duration':  (sell['date'] - buy['date']).days,\n",
    "            'profit': profit,\n",
    "            'profit_pct': \"{0:.2%}\".format(profit_pct)\n",
    "        }\n",
    "        rows.append(row)\n",
    "\n",
    "    df =  pd.DataFrame(rows, columns=['buy_date', 'duration', 'profit', 'profit_pct'])\n",
    "    with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n",
    "        print(df)\n",
    "\n",
    "    total_transaction = math.floor(len(signals) / 2)\n",
    "    stats = {\n",
    "        'total_transaction': total_transaction,\n",
    "        'profit_rate': \"{0:.2%}\".format(profit_count / total_transaction),\n",
    "        'avg_profit_per_transaction': \"{0:.2%}\".format(profit_pct_avg / total_transaction)\n",
    "    }\n",
    "    for key, value in stats.items():\n",
    "        print('{0:30}  {1}'.format(key, value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bffff420",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    buy_date  duration     profit profit_pct\n",
      "0 2021-05-25        55  26.127102     10.42%\n",
      "1 2021-07-23         5  -0.911564     -0.32%\n",
      "2 2021-08-19        13   9.602451      3.29%\n",
      "3 2021-10-08        38  41.194657     13.96%\n",
      "4 2021-12-27         8  -7.035004     -2.08%\n",
      "total_transaction               5\n",
      "profit_rate                     60.00%\n",
      "avg_profit_per_transaction      5.06%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x575 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "ticker_name = 'MSFT'\n",
    "yticker = yf.Ticker(ticker_name)\n",
    "data = yticker.history(period=\"1y\") # max, 1y, 3mo\n",
    "\n",
    "# macd\n",
    "data[\"macd\"], data[\"macd_signal\"], data[\"macd_hist\"] = ta.MACD(data['Close'])\n",
    "\n",
    "# plot macd\n",
    "macd_plot = mpf.make_addplot(data[\"macd\"], panel=1, color='fuchsia', title=\"MACD\")\n",
    "colors = ['g' if v >= 0 else 'r' for v in data[\"macd_hist\"]]\n",
    "macd_hist_plot = mpf.make_addplot(data[\"macd_hist\"], type='bar', panel=1, color=colors) # color='dimgray'\n",
    "macd_signal_plot = mpf.make_addplot(data[\"macd_signal\"], panel=1, color='b')\n",
    "\n",
    "# buy/sell\n",
    "buy_signals, sell_signals, signals = detect_macd_signals(data)\n",
    "\n",
    "# plot buy/sell\n",
    "buy_plot = mpf.make_addplot(buy_signals, type='scatter', marker='v', markersize=100, panel=0)\n",
    "sell_plot = mpf.make_addplot(sell_signals, type='scatter', marker='^', markersize=100, panel=0)\n",
    "\n",
    "# print buy/sell transaction and stats\n",
    "print_performance_summary(signals)\n",
    "\n",
    "# plot candle chart and all\n",
    "plots = [macd_plot, macd_signal_plot, macd_hist_plot, buy_plot, sell_plot]\n",
    "mpf.plot(data, type='candle', style='yahoo', addplot=plots, title=f\"\\n{ticker_name}\", ylabel='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "733bebaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We assume the cross-over of MACD and Signal line is the buy/sell trigger. We could run more tests to determine whether delaying N day after cross-over would yield better result.\n",
    "# Maybe we want to consider the weightage if the signal line is above 0 or below 0.\n",
    "# We should test with more stocks, either S&P 500 or NASDAQ 100.\n",
    "# We should experiment of using MACD together with other signals, such as SMA or Stochastic/RSI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a86d52a6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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